Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
In the development of multi-vehicle cooperative hardware-in-the-loop (HIL) simulation platforms based on machine vision, accurate vehicle pose estimation is crucial for achieving efficient cooperative control. However, monocular vision systems inevitably suffer from limited fields of view and insufficient image resolution during target detection, making it difficult to meet the requirements of large-scale, multi-target real-time perception. To address these challenges, this paper proposes an engineering-oriented multi-camera cooperative vision detection method, designed to maximize processing efficiency and real-time performance while maintaining detection accuracy. The proposed approach first projects the imaging results from multiple cameras onto a unified physical plane. By precomputing and caching the image stitching parameters, the method enables fast and parallelized image mosaicking. Experimental results demonstrate that, under typical vehicle speeds and driving angles, the stitched images achieve a 93.41% identification code recognition rate and a 99.08% recognition accuracy. Moreover, with high-resolution image (1440 × 960) inputs, the system can stably output 30 frames per second of stitched image streams, fully satisfying the dual requirements of detection precision and real-time processing for engineering applications....
The study proposes a methodological integration of machine vision and image processing based on color-based object detection. The primary goal of the study is to use the color vision method to simplify the process of transforming real objects into 3D digital twins for application in Sustainable Agriculture 4.0. The experiment solves several related problems: (1) Color analysis and methodology for quantifying the color representation of a 3D model. Representation quality was determined using colorimetric methods with sRGB and L*a*b* models in relation to the D65 standard. Colors with accurate color values on the object surface and in the 3D model were identified. (2) The process of capturing and creating digital twins using the SfM method is time-consuming and requires manual work. The study solves this problem by partially automating the entire process. The proposed DSLR system with an automated method for capturing, storing, and sorting data significantly accelerates the entire process. (3) To create a digital color scale, it is necessary to define the color values of 3D digital twins. A color segmentation procedure based on points on the surface of a 3D model is proposed. These color values form a basic color form corresponding to the color value changes in the coloring process of a real object. The proposed procedure uniquely integrates methodologies and has potential for use in Sustainable Agriculture 4.0. The proposed colorimetric method quantifies representation quality and could be deployed in other 3D model digitization and automation processes, especially in image processing and computer vision....
1. Tracking individuals in wild populations increasingly involves computer-aided analyses of photographic records. While several extant tools leverage naturally occurring patterns and marks as ‘fingerprints’ to distinguish individuals, these were chiefly designed with large or vertebrate taxa in mind. Insects and other small animals present opportunities for simplified image capture and processing due to their ease of handling, manipulation and the degree to which their identifying patterns are constrained on rigid exoskeletons or wings. 2. We present PlanarID, a Python pipeline and companion Shiny app for exploring, processing and analysing large-scale photographic records of invertebrates. The tool identifies individuals with distinctive colour-or pattern-based markings using established computer vision techniques. The pipeline applies colour-based thresholds to annotate focal patterns on wings, shells, or elytra, which are then used to recognise individuals across photograph-based capture–mark–recapture (CMR) datasets. 3. This image capture workflow and pipeline aim to reduce the need for complex image pre-processing and to provide an efficient, scalable tool for individual identification. Our Shiny app provides interactive tools to visualise all stages of ‘fingerprinting’, compare potential matches in a photographic record, quality-check the photographic record prior to analysis, and confirm individual matches following the pipeline execution. 4. Practical implication. We showcase PlanarID's core features using a sample photographic record of the burying beetle Nicrophorus vespilloides. We assess PlanarID's ability to assign identities to individuals using four distinct ‘fingerprinting’ algorithms. Our results indicate that PlanarID is a highly effective tool for identifying individuals via their unique, discrete colour patterns and generating capture histories from photographic records essential for further CMR analyses....
The routine culling of day-old male chicks represents a major ethical concern in the poultry industry. This practice has been banned in Germany, and a similar ban is being considered by the European Union. Each year, hundreds of millions of day-old male chicks are culled in the EU, with several billion culled worldwide. Various methods have been developed to determine the sex of chicks before hatching; however, most are invasive and identify sex relatively late, potentially after the onset of pain perception in embryos. Existing approaches include polymerase chain reaction analysis, spectroscopy, analysis of volatile organic compounds, morphological analysis, and machine vision. Previous studies have shown that machine vision can achieve accuracies of up to 89.25% by analyzing blood vessel patterns during early incubation. Despite this potential, research remains limited, particularly regarding different chicken breeds and the temporal development of embryos. In this study, we investigate the impact of both breed variation and temporal information on early-stage sex identification. Image data were collected on incubation days 4, 5, and 6 from a total of 208 chicken eggs. A convolutional neural network (CNN) and a hybrid convolutional neural network–recurrent neural network (CNN–RNN) model were evaluated to analyze spatial and temporal features. The results show that the CNN model achieved an accuracy of up to 71.43%, while the hybrid CNN–RNN model reached 67.85%. These findings indicate that incorporating temporal information did not improve performance compared to the baseline CNN. However, due to the limited size and quality of the dataset, no definitive conclusions can be drawn....
Precise grading is the foundation for improving the safety and consistency of Panax notoginseng in clinical applications. This study aims to propose a rapid, nondestructive quality grading method for Panax notoginseng based on machine vision and chemometrics. First, high-performance liquid chromatography (HPLC) was employed to determine the Panax Notoginseng Saponins (PNS) content of 143 samples. Based on hierarchical cluster analysis combined with the Elbow Rule, the samples were scientifically categorized into 3-Grade, 5-Grade, and 6-Grade standards. Five machine learning algorithms and six feature selection methods were compared to identify the optimal baseline model, and the Particle Swarm Optimization (PSO) algorithm was introduced to fine-tune the model’s hyperparameters. The final model was evaluated using 10-fold cross-validation, and a bench test was conducted on 50 samples for grading verification. Comparative analysis identified the pearson correlation coefficient combined with CatBoost (COR-CatBoost) as the optimal baseline model across all grading schemes. After hyperparameter fine-tuning with PSO, the finalCOR-CatBoost-PSO model achieved average classification accuracies of 98.6% ± 0.5%, 88.2% ± 1.2%, and 84.5% ± 1.5% for the 3-Grade, 5-Grade, and 6-Grade standards, respectively, via 10-fold cross-validation. The bench test results showed a 100% classification accuracy of P. notoginseng, an average offset of the robotic arm of 1.2 mm, and a single grading process time of 0.8–1.5 s. The results verify the reliability and effectiveness of the proposed rapid, non-destructive quality grading method for Panax notoginseng, which can provide technical support for improving the safety and consistency of Panax notoginseng in clinical applications....
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